IOD-CNN: Integrating Object Detection Networks for Event Recognition
This addresses the problem of improving event recognition accuracy for computer vision applications, but it is incremental as it builds on existing object detection methods.
The paper tackles event recognition by integrating architecturally different object detection networks into a unified CNN, resulting in enhanced performance for the task.
Many previous methods have showed the importance of considering semantically relevant objects for performing event recognition, yet none of the methods have exploited the power of deep convolutional neural networks to directly integrate relevant object information into a unified network. We present a novel unified deep CNN architecture which integrates architecturally different, yet semantically-related object detection networks to enhance the performance of the event recognition task. Our architecture allows the sharing of the convolutional layers and a fully connected layer which effectively integrates event recognition, rigid object detection and non-rigid object detection.